Finding boundary shape matching relationships in spatial data

  • Edwin M. Knorr
  • Raymond T. Ng
  • David L. Shilvock
Spatial Similarities
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1262)


This paper considers a new kind of knowledge discovery among spatial objects—namely that of partial boundary shape matching. Our focus is on mining spatial data, whereby many objects called features (represented as polygons) are compared with one or more point sets called clusters. The research described has practical application in such domains as Geographic Information Systems, in which a cluster of points (possibly created by an SQL query) is compared to many natural or man-made features to detect partial or total matches of the facing boundaries of the cluster and feature. We begin by using an alpha-shape to characterize the shape of an arbitrary cluster of points, thus producing a set of edges denoting the cluster's boundary. We then provide an approach for detecting a boundary shape match between the facing curves of the cluster and feature, and show how to quantify the value of the match. Optimizations and experimental results are also provided. We also describe several orientation strategies yielding significant performance enhancements. Finally, we show how top-k matches can be computed efficiently.


spatial knowledge analysis and discovery pattern matching GIS 


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  1. 1.
    R. Agrawal, S. Ghosh, T. Imielinski, B. Iyer, and A. Swami. “An Interval Classifier for Database Mining Applications”, Proceedings of the 18th VLDB Conference, pp. 560–573, 1992.Google Scholar
  2. 2.
    R. Agrawal, T. Imielinski, and A. Swami. “Mining Association Rules between Sets of Items in Large Databases”, Proceedings of the 1993 SIGMOD Conference, pp. 207–216, 1993.Google Scholar
  3. 3.
    H. Edelsbrunner, D. Kirkpatrick, and R. Seidel. “On the Shape of a Set of Points in the Plane”, IEEE Transactions on Information Theory, 29, 4, pp. 551–559, 1983.Google Scholar
  4. 4.
    M. Ester, H. Kriegel and X. Xu. “Knowledge Discovery in Large Spatial Databases: Focusing Techniques for Efficient Class Identification”, Proceedings of the 4th International Symposium on Large Spatial Databases (SSD'95), pp. 67–82, 1995.Google Scholar
  5. 5.
    W. J. Frawley, G. Piatetsky-Shapiro, and C. J. Matheus. “Knowledge Discovery in Databases: An Overview”, Knowledge Discovery in Databases, Piatetsky-Shapiro and Frawley (eds.), AAAI/MIT Press, pp. 1–27, 1991.Google Scholar
  6. 6.
    J. Han, Y. Cai, and N. Cercone. “Knowledge Discovery in Databases: an Attribute-Oriented Approach”, Proceedings of the 18th VLDB Conference, pp. 547–559, 1992.Google Scholar
  7. 7.
    D. Keim, H. Kriegel, and T. Seidl. “Supporting Data Mining of Large Databases by Visual Feedback Queries”, Proceedings of the 10th International Conference on Data Engineering, pp. 302–313, 1994.Google Scholar
  8. 8.
    D. Kirkpatrick and J. Radke. “A Framework for Computational Morphology”, Computational Geometry, G. Toussaint (ed.), The Netherlands: Elsevier Science Publishers B.V., 1985, pp. 217–248, 1985.Google Scholar
  9. 9.
    E. M. Knorr and R. T. Ng. “Finding Aggregate Proximity Relationships and Commonalities in Spatial Data Mining”, IEEE Transactions on Knowledge and Data Engineering, 8, 6, pp. 884–897, December, 1996.Google Scholar
  10. 10.
    W. Lu, J. Han, and B. C. Ooi. “Discovery of General Knowledge in Large Spatial Databases”, Proceedings of the Far East Workshop on Geographic Information Systems, Singapore, pp. 275–289, 1993.Google Scholar
  11. 11.
    R. Ng and J. Han. “Efficient and Effective Clustering Methods for Spatial Data Mining”, Proceedings of the 20th VLDB Conference, pp. 144–155, 1994.Google Scholar
  12. 12.
    R. Ng and Y. Yu. “Discovering Strong, Common and Discriminating Characteristics of Clusters from Thematic Maps”, Proceedings of the Eleventh Annual Symposium on Geographic Information Systems, pp. 392–394, 1997.Google Scholar
  13. 13.
    J. O'Rourke. Computational Geometry in C, Cambridge University Press, New York, 1994.Google Scholar
  14. 14.
    J. T. Schwartz and M. Sharir. “Identification of Partially Obscured Objects in Two and Three Dimensions by Matching Noisy Characteristic Curves”, International Journal of Robotics Research, Vol. 6, No. 2, pp. 29–44, Summer 1987.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Edwin M. Knorr
    • 1
  • Raymond T. Ng
    • 1
  • David L. Shilvock
    • 1
  1. 1.Department of Computer ScienceUniversity of British ColumbiaVancouverCanada

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